Anthropometric predictors of incident type 2 diabetes mellitus in Iranian women

BACKGROUND AND OBJECTIVES: Studies have shown a strong association between excess weight and risk of incident diabetes in Iranian women. Therefore, we investigated anthropometric indices in the prediction of diabetes in Iranian women. SUBJECTS AND METHODS: We examined 2801 females aged ≥220 years (mean [SD] age, 45.2 [12.9] years) in an Iranian urban population who were non-diabetic or had abnormal glucose tolerance at baseline. We estimated the predictive value of central obesity parameters (waist circumference [WC], waist-to-hip ratio [WHR], waist-to-height ratio [WHtR], body mass index [BMI]) in the prediction of diabetes. We classified each parameter in quartiles and compared the lowest with the highest quartile after adjusting for confounding variables, including age, hypertension, triglyceride levels, HDL-cholesterol, family history of diabetes, and abnormal glucose tolerance in a multivariate model. Receiver operator characteristic (ROC) curves were used to determine the predictive power of each variable. RESULTS: Over a median follow up of 3.5 years (11 months-6.3 years), 114 individuals developed diabetes (4.1%). The risk for developing diabetes was significantly higher for the highest quartile of BMI, WC, WHR and WHtR, respectively, compared to the lowest quartile, and the risk decreased but remained statistically significant when abnormal glucose tolerance was included in the multivariate model. WHtR had the highest area under the ROC curve. CONCLUSIONS: In Iranian women, BMI, WC, WHR, WHtR were predictive of development of type 2 diabetes, but WHtR was a better predictor than BMI.

O besity, which increases the risk of coronary heart disease, stroke and type 2 diabetes mell l litus (DM), is an important determinant of health. 1,2 The prevalence of obesity and overweight is increasing in developing countries, including Iran. 1,2 DM receives more attention than other related diseases both clinically and in public health. 3 A prospective epil l demiological study showed that increased abdominal fat accumulation is an independent risk factor for cardiovasl l cular disease. 4 Some studies have suggested that waist circumference (WC) is a better predictor for DM than other indicators of obesity. 5,6 Others have shown that the waistltolhip ratio (WHR) is the best predictive anl l thropometric variable for development of type 2 DM. 7,8 In a recent metalanalysis, Vazquez et al showed that body mass index (BMI), WC and WHR had a similar association with incident diabetes. 9 The ability of obel l sity indicators to predict diabetes may differ by ethnicl l Anthropometric predictors of incident type 2 diabetes mellitus in Iranian women ity, age and sex. 10,11 Our recent study in Iran showed that incident type 2 diabetes is largely attributable to being overweight, particularly in women. 12 In the Pima Indian population, BMI and waistltolheight ratio (WHtR) in men, and BMI, WC and WHtR in women were the best predictors of incident diabetes. 13 Recently, we showed that WHtR was better than BMI in identifying men at risk of diabetes. 14 This study was designed to determine the best anthropometric predictor of diabetes in a popul l lationlbased study in urban Iranian women.

METHODS
This study was conducted within the framework of the Tehran Lipid and Glucose Study (TLGS), a prospecl l tive study conducted on a representative sample of resil l dents of district 13 of Tehran (the age distribution and socioeconomic status of the population in district 13 is representative of the overall population of Tehran), with the aim of determining the prevalence of nonlcoml l municable disease risk factors and developing a healthy lifestyle to improve these risk factors. 15 In the TLGS, 15 010 people aged 3 years and older living in district 13 of Tehran were selected by a multistage cluster randoml sampling method. 15 They included 10 368 subjects aged ≥20 years evaluated in the crosslsectional phase 1 of TLGS. Phase 1 was a crosslsectional prevalence study of nonlcommunicable diseases and associated risk factors implemented from March 1999 to December 2001. Phase 2 was a prospective followlup study which had begun from 2002 to 2005, aiming to determine the trend of nonlcomunicable disease risk factors and incidence in a representative population. By the end of September 2005, 6246 individuals (59% females and 41% males) had participated in phase 2 of TLGS with a median followlup duration of 3.5 years (11 monthsl 6.3 years). From this population, 743 with diabetes (271 subjects with current use of a hypoglycemic agent and 472 with newly diagnosed diabetes according to the oral glucose tolerance test results [OGTT]) and 448 with missing data were excluded. Subjects with other forms of glucose intolerance such as impaired GTT or impaired fasting glucose were not excluded. Thus, from 5055 nonldiabetic subjects (2085 males and 2970 fel l males) at baseline, 2801 females with full data were inl l cluded in this study. The main reasons for lack of attenl l dance at follow up examinations despite repeated calls were either immigration (30%) or personal reasons. The Ethical Committee of The Endocrine Research Center of Shahid Beheshti University of Medical Sciences apl l proved the protocol for this study. Informed written consent was obtained from all subjects.
Subjects in each phase were interviewed privately and faceltolface by trained interviewers using preltestl l ed questionnaires. Initially, information on age, smoking habits, family history of diabetes, and medication use was collected. Subjects who reported a parent or sibling with diabetes were considered to have a positive faml l ily history of diabetes and those with a current or past history of smoking were designated as smokers. Weight was recorded to the nearest 100 grams while minimally clothed without shoes using digital scales. Height was measured in a standing position, without shoes, using a tape stadiometer with a minimum measurement of 1 mm, while the shoulders were in a normal state. BMI was calculated as weight in kilograms divided by height in meters squared. WC was recorded to the nearest 0.1 cm at the umbilical level and hip circumference at the maximal level over light clothing, using an unstretched tape meter, without pressure on the body surface. WHR was calculated as WC divided by hip circumference and WHtR as WC (cm) divided by height (cm). To avoid interobserver error, all measurements were taken by the same person. After the patient rested for 15 min, a qualified physician measured blood pressure, taking two measurements (one initial measurement for deterl l mining the peak inflation level) in a seated position usl l ing a standard mercury sphygmomanometer. There was at least a 30lsecond interval between these two sepal l rate measurements, and thereafter the mean of the two measurements was considered the participant' s blood pressure. At baseline and at each phase of the study, a blood sample was taken after a 12l14 hour overnight fast. Blood samples were taken in a sitting position acl l cording to the standard protocol and centrifuged within 30l45 min of collection. All blood analyses were done at the TLGS research laboratory on the day of blood collection. For the oral glucose tolerance test (OGTT), 82.5 g of glucose monohydrate solution (equivalent to 75 g anhydrous glucose) was administered orally to all subjects in each phase (excluding those with current use of a hypoglycemic agent) and plasma glucose was measured 2 hours later. The analysis of samples was performed using the Selectra 2 autolanalyzer (Vital Scientific, Spankeren, Netherlands). Fasting plasma glucose (FPG) and 2lhour postlload glucose (2hPG) were measured on the day of blood collection by the enl l zymatic colorimetric method using glucose oxidize. For lipid measurements, total cholesterol (TC) and triglycl l eride (TG) kits (Pars Azmoon Inc., Iran) were used. TC and TG were assayed using enzymatic colorimetric tests with cholesterol esterase and cholesterol oxidase, and glycerol phosphate oxidase, respectively. HDLl cholesterol (HDLlC) was measured after precipitation of the apolipoprotein B containing lipoproteins with phosphotungistic acid. All samples were analyzed when internal quality control met the acceptable criteria. Interland intralassay coefficients of variation were 0.5% and 2 for TC and HDLlC and 0.6% and 1.6 for TG, respectively.

Definition of variables and outcomes
Based on the fasting and 2lhour plasma glucose (2hPG) results, subjects were categorized according to American Diabetes Association (ADA) criteria as havl l ing impaired fasting glucose (IFG) (100 mg/dL ≤FPG <126 mg/dL), impaired glucose tolerance (IGT) (140 mg/dL ≤2hPG <200 mg/dL ), or diabetes (current use of hypoglycemic agent or FPG ≥126 mg/dL and/or 2hPG ≥200 mg/dL. Abnormal glucose tolerance was defined as having IFG or IGT. 16 Hypertension was del l fined as a systolic blood pressure ≥140 mm Hg and/ or diastolic blood pressure ≥90 mm Hg, or current

Statistical Analysis
Baseline variables were presented by followlup diabetes status. Data with normally distributed parameters are presented as means and standard deviations, whereas values for trigylcerides (TG) were logltransformed because of a skewed distribution and expressed as a geometric mean. The mean value and proportions of the baseline variables were compared between subjects who developed diabetes and those who did not using the t test and chilsquare test, respectively. To identify predictive factors for FPG over the period of followlup, multiple linear regression analysis was carried out. A logistic regression analysis using a stepwise conditional method was used to calculate the odds ratio (OR) and 95% confidence intervals (CI) for incident diabetes asl l sociated with quartiles of anthropometric variables in 2 models: Model 1 was a multivariate model adjusted for age, family history of diabetes, hypertension, HDLlC and TG. Model 2 was a full model, adjusted for the prel l vious variables plus abnormal glucose tolerance at the time of enrollment, considering that the latter is an iml l portant risk factor for diabetes. In each model, the subl l jects were categorized according to their WC, WHR, WHtR, and BMI quartiles. The first quartile was conl l sidered as a reference category with DM as outcome variable. Receiver operator characteristic (ROC) curves were used to compare the predictive power of each anl l thropometric variable after adjustment for age. All the statistical analyses except area under ROC comparisons were performed by SPSS 11.5 software package. The STATA software package version 8 was used to call l culate the ROC curve of each anthropometric variable and 95% confidence intervals. P values (2lsided) less than .05 were considered statistically significant.

RESULTS
The mean (SD) age of the women was 45. The baseline characteristics of the study subjects acl l cording to their followlup diabetes status are shown in Table 1 . Subjects who had developed diabetes at follow up had a significantly higher age, BMI, WC, WHR, WHtR, and a higher level of TG as well as lower HDLl C concentrations than nondiabetics. Diabetic women also had a higher prevalence of hypertension and a posil l tive family history of diabetes, IGT and IFG. Smoking status was not significantly different in those who devell l oped diabetes compared with those who did not devell l op diabetes. Each anthropometric index explained only about 11% of the variance in FPG after followlup in a multiple regression analysis (Table 2). When baseline FPG was added to this analysis, this variance increased by about 17% (R 2 =28%) (data not shown). In Table 3 the estimated OR and 95% CI for incident diabetes by quartiles of the anthropometric variables are presented for the two logistic regression models before and after adjustment for abnormal glucose tolerance. In the logisl l tic regression analysis, the ORs (and 95% CIs) in model 1 were 4.8 (2.1l10.9), 6.7 (2.6l17.1), 8.7 (3.0l24.7), and 8.0 (3.1l20.6) for the fourth quartile versus the first quartile for BMI, WC, WHR and WHtR, respectively. Also, the OR of incident diabetes increased across all Table 2. Multiple linear regression analyses between anthropometric and other independent variables with fasting plasma glucose as dependent variable. quartile of anthropometric indices (P for trend <.001). After further adjustment for abnormal glucose tolerl l ance (model 2) the OR (95% CI) of the highest quartile of BMI, WC, WHR and WHtR, decreased to 3.1(1.3l 7.2), 3.1 (1.1l8.3), 4.0, 3.3 respectively, compared to vall l ues in model 1, but remained significant. However, the OR for incident diabetes increased across all quartiles of anthropometric indices in the second model (P for trend <.05), except for WHR which remained marginl l ally significant (P for trend=0.05).

Body mass index
The baseline obesity indicators in this study were highly correlated with each other. BMI showed high correlation with WC (r=0.83), WHtR (r=0.83) and modest correlation with WHR (r=0.40). WHtR showed high correlation with both WC (r=0.96) and WHR (r=0.79) and finally WHR showed high correl l lation with WC (r=0.77) (data not shown). Therefore, because of the problem of collinearity it was not posl l sible to include them in the same regression model and ROC curve analysis was used to compare the predictive power of the different anthropometric variables. Figure  1 showed that only WHtR had a higher area under the ROC curve than BMI after adjustment for age (0.72 vs. 0.69). WC and WHR were considered equal to BMI in their power to predict type 2 diabetes. When the analyl l sis was restricted to overweight and obese subjects, none of the central obesity indicators was confirmed to be superior to BMI.

DISCUSSION
This prospective study in Iranian women showed that BMI, WC, WHR and WHtR can predict incident diabetes. Our model identified those with a 3l to 4lfold increase in likelihood of developing diabetes during a median follow up of 3.5 years (11 months to 6.3 years); however, the overall predictive discrimination, (as the area under the ROC curves showed), for diabetes was better for WHtR than BMI.
Excess body fat is a main cause of metabolic disl l turbances such as type 2 DM. 5 As a simple and nonl invasive method, anthropometric measurements have been used to assess general obesity (BMI) and central obesity (WC, WHR, WHtR). 19 BMI is reported as an indicator for identifying adults at risk of diabetes in many studies. 13,20 However, it has limitations bel l cause it does not distinguish overweight due to excess fat mass from lean mass. 21 In addition, some studies have shown that a high proportion of abdominal fat, particularly visceral fat, is a major risk factor for type 2 DM. 22 Therefore, other anthropometric parameters are used to assess excess visceral fat. WC and WHR are frequently used to estimate abdominal adipose tisl l   25 Lakka et al in prospective study, suggested WHR is a better index to predict corol l nary heart disease than WC and BMI. 26 Our full model ( Table 3) showed that the OR for incident diabetes for the highest quartile versus the lowest quartile was greater for WHR followed by WHtR, WC and BMI, although the confidence intervals are wide and overlapl l ping. In addition, despite a lower correlation with BMI and WC (r=0.40 and r=0.77, respectively), the WHR showed the same ability to predict diabetes as both BMI and WC, as discovered by Vazquez et al in a meta anall l ysis. 9 However, our data confirmed that none of these anthropometric parameters are good measures for prel l dicting future FPG (even when we considered baseline FPG). Considering the limitations of the OR (or relal l tive risks or hazard ratio) as a method of assessing the importance of risk factors and for a more comprehenl l sive picture of the clinical and public health relevance of anthropometric variables, we used ROC curve analyses to compare the predictive validity of these variables. 27 Among the central obesity variables only WHtR had a significantly larger area under the ROC curve than BMI. In line with our findings, Lin et al showed that in a Taiwanian population WHtR may be a better indical l tor for predicting cardiovascular risk factors than WC, WHR and BMI, especially for women. 28 Also, Lorenzo et al showed that area under the ROC curve for WHtR was better than WC for identifying diabetic women. 29 In a crosslsectional analysis, Schneider et al showed that WHtR may predict prevalent cardiovascular risk better than BMI, WC, and WHR. 30 Furthermore, our study highlights that in overweight and obese subjects no central obesity variable is supel l rior to BMI. Other studies suggest a stronger effect of body fat distribution on metabolic abnormality risk in normallweight individuals compared with overweight or obese subjects. 31,32 Our data indicate that the WHtR appears to be a better predictor of DM risk than BMI in the population of women as a whole. The WHtR is simple to assess and is easier to calculate (no squared term is used in the formula) and WC requires only the removal of clothing around the waist. In addition, waist measurement is more sensitive to diet and exercise than BMI because any increase in muscle mass might cause a slight change in BMI, but result in definite changes in WC and thus in WHtR.
This study had some limitations. First, about 40% of the participants in our baseline cohort were excluded from analysis due to loss at followup. This group was healthier in their baseline characteristics; therefore, we may have overestimated the incidence of diabetes in our population. Second, the duration of followlup was relal l tively short. Using a longer term follow up would prol l vide stronger evidence although a similarly short follow up was seen in other studies. 33,34 Finally, since chronic diseases are heterogeneous and multifactorial, factors other than anthropometric variables, such as hereditary factors and menopausal state and lifestylelrelated facl l tors, should be considered. 35 This was the first populal l tionlbased prospective study in Middle Eastern white women, which enhances the validity of our findings. In conclusion, abdominal obesity as measured by WHtR may be better predictors of type 2 diabetes compared to BMI in Iranian women. These simple, inexpensive and noninvasive measures of abdominal obesity is proposed to be incorporated in type 2 DM risk assessment.